Multivariate Analysis of Process Data using Robust Statistical Analysis and Variable Selection

摘要

Historical plant data are useful in developing multivariate statistical models for on-line process monitoring, soft sensors, and process troubleshooting. For the first two purposes, historical data are used to build a model to capture the normal characteristics of the process. However, the presence of outliers can adversely affect the model. Various robust statistical techniques are investigated in this paper for outlier identification. For process troubleshooting and fault identification, it is crucial to identify the key process variables that are associated with the root causes. Genetic algorithms (GA) are incorporated with Fisher discriminant analysis (FDA) for this purpose. These techniques have been successfully applied at The Dow Chemical Company.